Knowledge Graph Market size was valued at USD 1 billion in 2022 and is predicted to register at a CAGR of over 13.5% between 2023 and 2032. The integration of root cause analysis platforms with AI tools and knowledge graphs in monitoring IT environments is driving the market progression. These platforms utilize advanced algorithms and knowledge graphs to efficiently identify and analyze issues in IT systems.
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For instance, in July 2023, Webb.ai launched an early access for the continuous automated root cause analysis platform. The platform employs a combination of generative AI and machine learning algorithm capabilities derived from Large Language Models (LLMs) to consolidate alerts into advanced insights. These insights are then presented in natural language to DevOps team members.
|Knowledge Graph Market Size in 2022:
|USD 1 Billion
|2023 to 2032
|Forecast Period 2023 to 2032 CAGR:
|2032 Value Projection:
|USD 3.7 Billion
|Historical Data for:
|2018 to 2022
|No. of Pages:
|Tables, Charts & Figures:
|Type, Task Type, Data Source, Organization Size, Application, End-use, and Region
|Pitfalls & Challenges:
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Applications such as search engines, recommendation systems, and data integration are fueling the demand for the market. Search engines utilize knowledge graphs to provide more accurate and relevant search results. Recommendation systems employ these graphs to understand user preferences and deliver personalized content. In data integration, knowledge graphs facilitate the seamless connection of diverse data sources. These applications highlight the versatility and efficiency of knowledge graphs, driving their adoption across various sectors and boosting their market demand.
Maintaining high-quality & consistent data poses significant challenges in the knowledge graph market. Integrating information from diverse sources often results in varied data formats and standards. Inaccurate or inconsistent data can lead to flawed analyses and unreliable insights. Ensuring data quality requires meticulous cleansing, transformation, and validation processes. Additionally, as knowledge graphs continually evolve, sustaining data accuracy becomes an ongoing challenge, demanding robust governance frameworks and continuous monitoring to maintain the integrity of the interconnected information within the graph.